The work presented in this thesis aims at validating an original multicriteria performances estimation tool, NESSIE, dedicated to the prediction of performances to accelerate the design of electronic embedded systems. This tool has been developed in a previous thesis to cope with the limitations of existing design tools and offers a new solution to face the growing complexity of the current applications and electronic platforms and the multiple constraints they are subjected to. More precisely, the goal of the tool is to propose a flexible framework targeting embedded systems in a generic way and enable a fast exploration of the design space based on the estimation of user-defined criteria and a joint hierarchical representation of the application and the platform. In this context, the purpose of the thesis is to put the original framework NESSIE to the test to analyze if it is indeed useful and able to solve current design problems. Hence, the dissertation presents : - A study of the State-of-the-Art related to the existing design tools. I propose a classification of these tools and compare them based on typical criteria. This substantial survey completes the State-of-the-Art done in the previous work. This study shows that the NESSIE framework offers solutions to the limitations of these tools. - The framework of our original mapping tool and its calculation engine. Through this presentation, I highlight the main ingredients of the tool and explain the implemented methodology. - Two external case studies that have been chosen to validate NESSIE and that are the core of the thesis. These case studies propose two different design problems (a reconfigurable processor, ADRES, applied to a matrix multiplication kernel and a 3D stacking MPSoC problem applied to a video decoder) and show the ability of our tool to target different applications and platforms. The validation is performed based on the comparison of a multi-criteria estimation of the performances for a significant amount of solutions, between NESSIE and the external design flow. In particular, I discuss the prediction capability of NESSIE and the accuracy of the estimation. -The study is completed, for each case study, by a quantification of the modeling time and the design time in both flows, in order to analyze the gain achieved by our tool used upstream from the classical tool chain compared to the existing design flow alone. The results showed that NESSIE is able to predict with a high degree of accuracy the solutions that are the best candidates for the design in the lower design flows. Moreover, in both case studies, modeled respectively at a low and higher abstraction level, I obtained a significant gain in the design time. However, I also identified limitations that impact the modeling time and could prevent an efficient use of the tool for more complex problems. To cope with these issues, I end up by proposing several improvements of the framework and give perspectives to further develop the tool.

Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.

Immunoglobulin E (IgE) mediated allergy is a major and seemingly increasing health problem in the Western countries. The combined usage of databases of molecular and clinical information on allergens (allergenic proteins) as well as new experimental platforms capable of generating huge amounts of allergy-related data from a single blood test holds great potential to enhance our knowledge of this complex disease. To maximally benefit from this development, however, both novel and improved methods for computational analysis are urgently required. This thesis concerns two types of important and practical computational analyses of allergens: allergenicity/IgE-cross-reactivity risk assessment and characterization of IgE-reactivity patterns. Both directions rely on development and implementation of bioinformatics and statistical learning algorithms, which are applied to either amino acid sequence information of allergenic proteins or on quantified human blood serum levels of specific IgE-antibodies to allergen preparations (purified extracts of allergenic sources, such as e.g. peanut or birch). The main application for computational risk assessment of allergenicity is to prevent unintentional introduction of allergen-encoding transgenes in genetically modified (GM) food crops. Two separate classification procedures for potential protein allergenicity are introduced. Both protocols rely on multivariate classification algorithms that are educated to discriminate allergens from presumable non-allergens based on their amino acid sequence. Both classification procedures are thoroughly evaluated and the second protocol shows state-of-the-art performance in comparison to current top-ranked methods. Moreover, several pitfalls in performance estimation of classifiers are demonstrated and procedures to circumvent these are suggested. Visualization and characterization of IgE-reactivity patterns among allergen preparations are enabled by application of bioinformatics and statistical learning methods to a multivariate dataset holding recorded blood serum IgE-levels of over 1000 sensitized individuals, each measured to 89 allergen preparations. Moreover, a novel framework for divisive hierarchical clustering including graphical representation of the resulting output is introduced, which greatly simplifies analysis of the abovementioned dataset. Important IgE-reactivity relationships within several groups of allergen preparations are identified including well-known groups of clinically relevant cross-reactivities.

Nowadays the designs of HW/SW are extremely complex. HW/SW co-verification is really difficult, consequently the new design layer, Electronic-System Level (ESL), is proposed to replace the original design flow. Today¡¦s ESL can verify the whole system simulation include the Processor, Bus, Memory¡K such as the HWs. It also can run a small program on the system. But it is hard to verify the larger program - such as the operation system because the limitations of the simulation speed. Currently some people proposed the QEMU-SystemC virtual platform. It can greatly speed up the CPU simulation speed. But the abstract simulated CPU has no timing information. It is infeasible to explore the system execution time and performance. We proposed the method: CPU, Cache, TLB and SDRAM with timing model; connect the CPU and the designed HW in TLM bus module in the HW/SW co-simulation. We can analyze the performance in the estimated timing information, and it will not take many simulation times. In addition, we developed the analysis program to show the execution time in each program block. It can help designer to locate the performance bottleneck quickly in the complex HW/SW. A case study is the 3D graphic SoC. We find the performance bottleneck in HW/SW design according the performance information purposed by our work.

Atualmente, novas metodologias de projeto são necessárias devido a crescente complexidade dos sistemas embarcados. Metodologias no nível de sistema são propostas para auxiliar o projetista a lidar com a crescente complexidade, iniciando o projeto em um nível de abstração mais alto que o nível de transferência de registradores. Ferramentas de estimativa de desempenho são uma importante parte das metodologias no nível de sistema, visto que as mesmas auxiliam a exploração do espaço de projeto desde os estágios iniciais. O objetivo desta tese é definir uma metodologia integrada para estimativa de desempenho do software. Atualmente, nota-se a crescente utilização de software embarcado, inclusive utilizando múltiplos processadores, visando atender os requisitos de flexibilidade, desempenho e potência consumida. O desenvolvimento de estimadores de desempenho de software não é trivial, devido à utilização de processadores embarcados com arquiteturas avançadas. Para auxiliar a seleção do processador no nível da especificação do sistema, um novo modelo de estimador do desempenho do software baseado em redes neurais é proposto. Redes neurais mostraram-se uma solução adequada para uma rápida estimativa de desempenho em um estágio inicial do projeto. Para realizar a análise do desempenho do software no nível funcional do barramento, onde o mapeamento do hardware e software já está definido, é utilizado um modelo global de simulação, chamado de protótipo virtual. A metodologia de análise de desempenho proposta neste trabalho é integrada a um ambiente para refinamento de interfaces de hardware e software chamada ROSES. A metodologia proposta é avaliada através de um estudo de caso de uma arquitetura multiprocessada de um codificador MPEG4. / Nowadays, embedded system complexity requires new design methodologies. System-level methodologies are proposed to cope with this complexity, starting the design above the register-transfer level. Performance estimation tools are an important piece of system-level design methodologies, since they are used to aid design space exploration at an early design stage. The goal of this thesis is to define an integrated methodology for software performance estimation. Currently, embedded software usage is increasing, becoming multiprocessor system-on-chip a common solution to cope with flexibility, performance, and power requirements. The development of accurate software performance estimators is not trivial, due to the increased complexity of embedded processors. To drive processor selection at specification level, a novel analytic software performance estimator based on neural networks is proposed. The neural network enables a fast estimation at an early design stage. To target the software performance analysis at bus functional level, where mapping of the hardware and software components is already established, we use a global simulation model supporting performance profiling. The proposed software performance estimation methodology is linked to a hardware and software interface refinement environment named ROSES. The proposed methodology is evaluated through a case study of a multiprocessor MPEG4 encoder.

Atualmente, novas metodologias de projeto são necessárias devido a crescente complexidade dos sistemas embarcados. Metodologias no nível de sistema são propostas para auxiliar o projetista a lidar com a crescente complexidade, iniciando o projeto em um nível de abstração mais alto que o nível de transferência de registradores. Ferramentas de estimativa de desempenho são uma importante parte das metodologias no nível de sistema, visto que as mesmas auxiliam a exploração do espaço de projeto desde os estágios iniciais. O objetivo desta tese é definir uma metodologia integrada para estimativa de desempenho do software. Atualmente, nota-se a crescente utilização de software embarcado, inclusive utilizando múltiplos processadores, visando atender os requisitos de flexibilidade, desempenho e potência consumida. O desenvolvimento de estimadores de desempenho de software não é trivial, devido à utilização de processadores embarcados com arquiteturas avançadas. Para auxiliar a seleção do processador no nível da especificação do sistema, um novo modelo de estimador do desempenho do software baseado em redes neurais é proposto. Redes neurais mostraram-se uma solução adequada para uma rápida estimativa de desempenho em um estágio inicial do projeto. Para realizar a análise do desempenho do software no nível funcional do barramento, onde o mapeamento do hardware e software já está definido, é utilizado um modelo global de simulação, chamado de protótipo virtual. A metodologia de análise de desempenho proposta neste trabalho é integrada a um ambiente para refinamento de interfaces de hardware e software chamada ROSES. A metodologia proposta é avaliada através de um estudo de caso de uma arquitetura multiprocessada de um codificador MPEG4. / Nowadays, embedded system complexity requires new design methodologies. System-level methodologies are proposed to cope with this complexity, starting the design above the register-transfer level. Performance estimation tools are an important piece of system-level design methodologies, since they are used to aid design space exploration at an early design stage. The goal of this thesis is to define an integrated methodology for software performance estimation. Currently, embedded software usage is increasing, becoming multiprocessor system-on-chip a common solution to cope with flexibility, performance, and power requirements. The development of accurate software performance estimators is not trivial, due to the increased complexity of embedded processors. To drive processor selection at specification level, a novel analytic software performance estimator based on neural networks is proposed. The neural network enables a fast estimation at an early design stage. To target the software performance analysis at bus functional level, where mapping of the hardware and software components is already established, we use a global simulation model supporting performance profiling. The proposed software performance estimation methodology is linked to a hardware and software interface refinement environment named ROSES. The proposed methodology is evaluated through a case study of a multiprocessor MPEG4 encoder.

L'intégration de plusieurs processeurs hétérogènes en un seul système sur puce (SoC) est une tendance claire dans les systèmes embarqués. La conception et la vérification de ces systèmes nécessitent des plateformes rapides de simulation, et faciles à construire. Parmi les approches de simulation de logiciels, la simulation native est un bon candidat grâce à l'exécution native de logiciel embarqué sur la machine hôte, ce qui permet des simulations à haute vitesse, sans nécessiter le développement de simulateurs d'instructions. Toutefois, les techniques de simulation natives existantes exécutent le logiciel de simulation dans l'espace de mémoire partagée entre le matériel modélisé et le système d'exploitation hôte. Il en résulte de nombreux problèmes, par exemple les conflits l'espace d'adressage et les chevauchements de mémoire ainsi que l'utilisation des adresses de la machine hôte plutôt des celles des plates-formes matérielles cibles. Cela rend pratiquement impossible la simulation native du code existant fonctionnant sur la plate-forme cible. Pour surmonter ces problèmes, nous proposons l'ajout d'une couche transparente de traduction de l'espace adressage pour séparer l'espace d'adresse cible de celui du simulateur de hôte. Nous exploitons la technologie de virtualisation assistée par matériel (HAV pour Hardware-Assisted Virtualization) à cet effet. Cette technologie est maintenant disponibles sur plupart de processeurs grande public à usage général. Les expériences montrent que cette solution ne dégrade pas la vitesse de simulation native, tout en gardant la possibilité de réaliser l'évaluation des performances du logiciel simulé. La solution proposée est évolutive et flexible et nous fournit les preuves nécessaires pour appuyer nos revendications avec des solutions de simulation multiprocesseurs et hybrides. Nous abordons également la simulation d'exécutables cross- compilés pour les processeurs VLIW (Very Long Instruction Word) en utilisant une technique de traduction binaire statique (SBT) pour généré le code natif. Ainsi il n'est pas nécessaire de faire de traduction à la volée ou d'interprétation des instructions. Cette approche est intéressante dans les situations où le code source n'est pas disponible ou que la plate-forme cible n'est pas supporté par les compilateurs reciblable, ce qui est généralement le cas pour les processeurs VLIW. Les simulateurs générés s'exécutent au-dessus de notre plate-forme basée sur le HAV et modélisent les processeurs de la série C6x de Texas Instruments (TI). Les résultats de simulation des binaires pour VLIW montrent une accélération de deux ordres de grandeur par rapport aux simulateurs précis au cycle près. / Integration of multiple heterogeneous processors into a single System-on-Chip (SoC) is a clear trend in embedded systems. Designing and verifying these systems require high-speed and easy-to-build simulation platforms. Among the software simulation approaches, native simulation is a good candidate since the embedded software is executed natively on the host machine, resulting in high speed simulations and without requiring instruction set simulator development effort. However, existing native simulation techniques execute the simulated software in memory space shared between the modeled hardware and the host operating system. This results in many problems, including address space conflicts and overlaps as well as the use of host machine addresses instead of the target hardware platform ones. This makes it practically impossible to natively simulate legacy code running on the target platform. To overcome these issues, we propose the addition of a transparent address space translation layer to separate the target address space from that of the host simulator. We exploit the Hardware-Assisted Virtualization (HAV) technology for this purpose, which is now readily available on almost all general purpose processors. Experiments show that this solution does not degrade the native simulation speed, while keeping the ability to accomplish software performance evaluation. The proposed solution is scalable as well as flexible and we provide necessary evidence to support our claims with multiprocessor and hybrid simulation solutions. We also address the simulation of cross-compiled Very Long Instruction Word (VLIW) executables, using a Static Binary Translation (SBT) technique to generated native code that does not require run-time translation or interpretation support. This approach is interesting in situations where either the source code is not available or the target platform is not supported by any retargetable compilation framework, which is usually the case for VLIW processors. The generated simulators execute on top of our HAV based platform and model the Texas Instruments (TI) C6x series processors. Simulation results for VLIW binaries show a speed-up of around two orders of magnitude compared to the cycle accurate simulators.

Atualmente, novas metodologias de projeto são necessárias devido a crescente complexidade dos sistemas embarcados. Metodologias no nível de sistema são propostas para auxiliar o projetista a lidar com a crescente complexidade, iniciando o projeto em um nível de abstração mais alto que o nível de transferência de registradores. Ferramentas de estimativa de desempenho são uma importante parte das metodologias no nível de sistema, visto que as mesmas auxiliam a exploração do espaço de projeto desde os estágios iniciais. O objetivo desta tese é definir uma metodologia integrada para estimativa de desempenho do software. Atualmente, nota-se a crescente utilização de software embarcado, inclusive utilizando múltiplos processadores, visando atender os requisitos de flexibilidade, desempenho e potência consumida. O desenvolvimento de estimadores de desempenho de software não é trivial, devido à utilização de processadores embarcados com arquiteturas avançadas. Para auxiliar a seleção do processador no nível da especificação do sistema, um novo modelo de estimador do desempenho do software baseado em redes neurais é proposto. Redes neurais mostraram-se uma solução adequada para uma rápida estimativa de desempenho em um estágio inicial do projeto. Para realizar a análise do desempenho do software no nível funcional do barramento, onde o mapeamento do hardware e software já está definido, é utilizado um modelo global de simulação, chamado de protótipo virtual. A metodologia de análise de desempenho proposta neste trabalho é integrada a um ambiente para refinamento de interfaces de hardware e software chamada ROSES. A metodologia proposta é avaliada através de um estudo de caso de uma arquitetura multiprocessada de um codificador MPEG4. / Nowadays, embedded system complexity requires new design methodologies. System-level methodologies are proposed to cope with this complexity, starting the design above the register-transfer level. Performance estimation tools are an important piece of system-level design methodologies, since they are used to aid design space exploration at an early design stage. The goal of this thesis is to define an integrated methodology for software performance estimation. Currently, embedded software usage is increasing, becoming multiprocessor system-on-chip a common solution to cope with flexibility, performance, and power requirements. The development of accurate software performance estimators is not trivial, due to the increased complexity of embedded processors. To drive processor selection at specification level, a novel analytic software performance estimator based on neural networks is proposed. The neural network enables a fast estimation at an early design stage. To target the software performance analysis at bus functional level, where mapping of the hardware and software components is already established, we use a global simulation model supporting performance profiling. The proposed software performance estimation methodology is linked to a hardware and software interface refinement environment named ROSES. The proposed methodology is evaluated through a case study of a multiprocessor MPEG4 encoder.

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